Search for tag: "learning"
Understanding PAG and NAG (Potential Acoustic Gain) (Needed Acoustic Gain)Is the Audio System Stable? Is it free from 'ringing' and 'feedback'? We can calculate this to give us the best results out of our Audio System using PAG and NAG math formulas
From Chris Robinson
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Securing Cisco Switch - Basic SetupSecuring the Cisco Switch using the CLI. Learn how to secure access to Console Connection and to Privilege Exec Mode. Secure Virtual terminal for remote access. Lastly, we'll encrypt all of our…
From Chris Robinson
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Reuth Mirsky - The Seeing Eye Robot: Developing a Human-Aware Artificial CollaboratorAutomated care systems are becoming more tangible than ever: recent breakthroughs in robotics and machine learning can be used to address the need for automated care created by the increasing aging…
From Katie Gentilello
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Arthur Gretton – Generalized Energy-Based ModelsArthur Gretton will describe Generalized Energy Based Models (GEBM) for generative modeling. These models combine two trained components: a base distribution (generally an implicit model, as in a…
From Katie Gentilello
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Francis Bach - Structured Prediction - Beyond Support Vector Machine and Cross EntropyMany classification tasks in machine learning lie beyond the classical binary and multi-class classification settings. In those tasks, the output elements are structured objects made of…
From Katie Gentilello
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Alekh Agarwal - Towards a Theory of Representation Learning for Reinforcement LearningProvably sample-efficient reinforcement learning from rich observational inputs remains a key open challenge in research. While impressive recent advances have allowed the use of linear modelling…
From Katie Gentilello
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Jie Tan - Learning Locomotion: From Simulation to Real WorldDeep Reinforcement Learning (DRL) holds the promise of designing complex robotic controllers automatically. In this talk, I will discuss two different approaches to apply deep reinforcement learning…
From Katie Gentilello
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Qi Wei - Generative models based on point processes for financial time series simulationIn this seminar, I will talk about generative models based on point processes for financial time series simulation. Specifically, we focus on a recently developed state-dependent Hawkes (sdHawkes)…
From Katie Gentilello
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Ellie Pavlick - You can lead a horse to water...: Representing vs. Using Features in Neural NLPA wave of recent work has sought to understand how pretrained language models work. Such analyses have resulted in two seemingly contradictory sets of results. On one hand, work based on…
From Katie Gentilello
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Csaba Szepesvari - Compressed computation of good policies in large MDPsMarkov decision processes (MDPs) is a framework that captures keys aspect of many real-world decision making problems with the few assumptions. Unfortunately, as a result MDPs lack structure and as…
From Katie Gentilello
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Bolei Zhou - Interpretable latent space and inverse problem in deep generative modelsRecent progress in deep generative models such as Generative Adversarial Networks (GANs) has enabled synthesizing photo-realistic images, such as faces and scenes. However, it remains much less…
From Katie Gentilello
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Jia-Bin Huang - Bringing Visual Memories to LifePhotography allows us to capture and share memorable moments of our lives. However, 2D images appear flat due to the lack of depth perception and may suffer from poor imaging conditions such as…
From Katie Gentilello
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ML@GT Lab presents LAB LIGHTNING TALKS 2020Labs affiliated with the Machine Learning Center at Georgia Tech (ML@GT) will have the opportunity to share their research interests, work, and unique aspects of their lab in three minutes or less to…
From Katie Gentilello
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Let's Talk About Bias and Diversity in Data, Software, and InstitutionsBias and lack of diversity have long been deep-rooted problems across industries. We discuss how these issues impact data, software, and institutions, and how we can improve moving forward.The panel…
From Katie Gentilello
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Ankur Parikh - Towards High Precision Text GenerationDespite large advances in neural text generation in terms of fluency, existing generation techniques are prone to hallucination and often produce output that is unfaithful or irrelevant to the source…
From Katie Gentilello
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